AI/ML Engineer
Job in
Reston, Fairfax County, Virginia, 22090, USA
Listed on 2026-03-12
Listing for:
Interon IT Solutions
Full Time
position Listed on 2026-03-12
Job specializations:
-
Software Development
AI Engineer, Machine Learning/ ML Engineer, Data Engineer, Python
Job Description & How to Apply Below
Job Title: AI/ML Engineer (Python, AWS, GenAI)
Location: Reston, VA (In-person interviews required)
Candidate Requirement: Local to East Coast only
Job DescriptionClient is seeking an AI/ML Engineer to design and deliver end-to-end AI/ML and Generative AI solutions in a cloud-native AWS environment. The ideal candidate will have strong Python engineering skills, experience deploying ML/GenAI workloads on AWS, and hands‑on expertise building RAG pipelines and MLOps workflows.
Responsibilities- Design and implement end-to-end AI/ML and Generative AI solutions using Python, including training, evaluation, optimization, and deployment.
- Build and maintain cloud-native applications on AWS using Lambda, ECS/Fargate, S3, API Gateway, Dynamo
DB, RDS/Aurora, Sage Maker, and Bedrock. - Develop scalable, high-performance Python microservices using FastAPI/Flask for data pipelines, real-time analytics, and model inference.
- Architect and operationalize RAG pipelines, embeddings, vector databases, and LLM-powered solutions (chatbots, summarization, semantic search, anomaly detection).
- Implement CI/CD pipelines (Git Hub/Git Lab/Code Pipeline) and Infrastructure-as-Code using Terraform/Cloud Formation.
- Build and support MLOps workflows: model versioning, containerized training/inference, automated retraining, monitoring, and performance tuning.
- Strong hands-on experience in Python development for AI/ML and GenAI solutions.
- Proven experience working with AWS services for application and ML deployments.
- Experience building APIs/microservices using FastAPI or Flask.
- Hands‑on experience with RAG, embeddings, and vector databases.
- Experience with CI/CD and IaC (Terraform/Cloud Formation).
- Strong understanding of MLOps practices and production model monitoring.
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